Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods / 부인종양
Journal of Gynecologic Oncology
;
: e65-2019.
Artículo
en Inglés
| WPRIM
| ID: wpr-764519
ABSTRACT
OBJECTIVES:
The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.METHODS:
Information of EOC patients from Samsung Medical Center (training cohort, n=1,128) was analyzed to optimize the prognostic model using GB. The performance of the final model was externally validated with patient information from Asan Medical Center (validation cohort, n=229). The area under the curve (AUC) by the GB model was compared to that of the conventional Cox proportional hazard regression analysis (CoxPHR) model.RESULTS:
In the training cohort, the AUC of the GB model for predicting second year overall survival (OS), with the highest target value, was 0.830 (95% confidence interval [CI]=0.802–0.853). In the validation cohort, the GB model also showed high AUC of 0.843 (95% CI=0.833–0.853). In comparison, the conventional CoxPHR method showed lower AUC (0.668 (95% CI=0.617–0.719) for the training cohort and 0.597 (95% CI=0.474–0.719) for the validation cohort) compared to GB. New classification according to survival probability scores of the GB model identified four distinct prognostic subgroups that showed more discriminately classified prediction than the International Federation of Gynecology and Obstetrics staging system.CONCLUSION:
Our novel GB-guided classification accurately identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method. This approach would be useful for accurate estimation of individual outcomes of EOC patients.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Neoplasias Ováricas
/
Pronóstico
/
Estudios de Cohortes
/
Clasificación
/
Antígeno Ca-125
/
Área Bajo la Curva
/
Aprendizaje Automático
/
Ginecología
/
Métodos
/
Obstetricia
Tipo de estudio:
Estudio de etiología
/
Estudio de incidencia
/
Estudio observacional
/
Estudio pronóstico
/
Factores de riesgo
Límite:
Humanos
Idioma:
Inglés
Revista:
Journal of Gynecologic Oncology
Año:
2019
Tipo del documento:
Artículo
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